Cross-country policy comparison of 30 km/h speed limits
Bibliographic record
Abstract
30 km/h speed zones are one of the most cost-effective road safety interventions to enhance safety and liveability of local streets. In recognition, they have been recommended for implementation globally by the World Health Organisation, and a number of areas globally have already implemented 30 km/h speed zones. However, only two zones are currently implemented in the state of Victoria, Australia, and these zones are not widely adopted across Australia. Greater understanding of the barriers to implementation is needed to rapidly advance implementation of this effective road safety intervention. We aimed to identify and explore barriers and enablers of implementation of 30 km/h speed limits in the state of Victoria, Australia, and compare this to implementation in an area where 30 km/h speed zones have been implemented successfully – British Columbia, Canada. We conducted 26 semi-structured interviews with relevant policy and implementation partners across government departments and local councils in Victoria, Australia and British Columbia, Canada to identify and explore barriers to and enablers of successful implementation of 30 km/h speed limits. Data were analysed abductively through reflexive thematic analysis. Six key themes were identified: (i) Appetite for change, describing recognition of importance of 30 km/h speed limits throughout state and local governments; (ii) Policy misalignment, describing a discrepancy between recognition of the importance of 30 km/h speed limits, and limitations in policy to implement them; (iii) Lack of local evidence, describing how a lack of local evidence of what road environments are suitable for 30 km/h speeds limits implementation; (iv) Council capacity, describing the stretched capacity of local government, where responsibility of delivery of 30 km/h speed limits lie; (v) Need for ‘self-explaining’ roads, describing a need for a whole of road environment approach for targeting 30 km/h speeds, rather than signage changes only, and (vi) Equity lessons in implementation, which detailed the unintended effects learned through implementation. Across local and state government, there is promising momentum and support for 30 km/h speed zones. However, state government policy reform is needed to enable easier implementation by local councils. This study provides critical insights into the complexity of implementing road safety interventions and opportunities to enhance systems to catalyse change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".